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Metabolomics: A Global Biochemical Approach to the Study of Central Nervous System Diseases Rima Kaddurah-Daouk* ,1 and K Ranga Rama Krishnan 1 1 Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA Metabolomics, the omics science of biochemistry, is a global approach to understanding regulation of metabolic pathways and metabolic networks of a biological system. Metabolomics complements data derived from genomics, transcriptomics, and proteomics to assist in providing a systems approach to the study of human health and disease. In this review we focus on applications of metabolomics for the study of diseases of the nervous system. We share concepts in metabolomics, tools used in metabolic profiling and early findings from the study of neuropsychiatric diseases, and drugs used to treat these diseases. Metabolomics emerges as another powerful tool in central nervous system research. Neuropsychopharmacology Reviews (2009) 34, 173–186; doi:10.1038/npp.2008.174; published online 8 October 2008 Keywords: metabolomics; pharmacometabolomics; metabonomics; biomarkers; neurodegenerative; psychiatric diseases INTRODUCTION Millions of people suffer from mental illnesses or neurode- generative diseases such as Parkinson’s disease (PD), Alzheimer’s disease, schizophrenia, depression, addiction, autism, dyslexia, and learning disabilities. These, among other diseases, are in much need for better treatments. Unfortunately, our current understanding of these disorders is limited. In fact it is likely that most of these disorders are not unitary conditions but may be a conglomeration of entities that are yet to be defined. Although some progress has been made in the treatment of psychiatric disorders many patients do not respond to current therapies. We also cannot predict who will respond to which treatment. This inherent variability coupled with intrinsic differences in pharmacology is the underlying factor that affects our inability to predict how an individual patient may respond to a selected therapy. Such uncertainty is distressing for patients and families who engage repeatedly in ‘trial-and- error’ choices in search of ‘the right fit’ and for clinicians thus resorting to widespread switching of medications (Weiden and Buckley, 2007) and polypharmacy (Tranulis et al, 2008). The inordinately high personal and societal burden of inadequate trial-and-error management under- scores an urgent need for validated biomarkers that establish diagnosis, guide drug selection, and reliably predict response to treatment (Insel, 2007; Lieberman et al, 2005). To understand brain function and its complexity new ideas, new approaches, and new technologies are much needed. Efforts to scale-up data production and analysis in neuroscience have been escalating. Genomics including comparative genomics, gene expression atlases and the organization of genome-scale projects, gene microarrays, proteomics, monitoring the activity of individual neurons using multiple-electrode recordings, and imaging studies have all provided powerful approaches to the study of neurological disorders (articles in Nat Neurosci 7, 425, published in 2004). Metabolomics the newest of the omics approaches provides powerful tools for defining perturba- tions in metabolic pathways and networks in human disease (Kaddurah-Daouk et al, 2008; Kristal et al, 2007a, b; Lindon et al, 2007; Harrigan and Goodacre, 2003). The metabolome defines a metabolic state as regulated by net interactions between gene and environment influences and provides information that can possibly bridge the gap between genotype and phenotype. It provides a missing piece to a systems approach to the study of diseases of the central nervous system (CNS). Metabolic signatures for CNS disorders could result in the identification of biomarkers for disease, for disease progression or for response to therapy. In addition metabolomics provides powerful tools for the process of drug discovery and drug development by providing detailed biochemical knowledge about drug candidates, their mechanism of action, therapeutic poten- tial, and side effects as exemplified in this review. Received 30 July 2008; revised 31 August 2008; accepted 31 August 2008 *Correspondence: Dr R Kaddurah-Daouk, Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, DUMC 3903, Durham, NC 27710, USA, Tel: + 919-684-2611, Fax: + 919-681-7668; E-mail: [email protected] Neuropsychopharmacology REVIEWS (2009) 34, 173–186 & 2009 Nature Publishing Group All rights reserved 0893-133X/09 $30.00 ............................................................................................................................................................... www.neuropsychopharmacology.org 173 REVIEW .............................................................................................................................................. Neuropsychopharmacology REVIEWS

Transcript of Metabolomics: A Global Biochemical Approach to the … · Metabolomics: A Global Biochemical...

Metabolomics: A Global Biochemical Approach to theStudy of Central Nervous System Diseases

Rima Kaddurah-Daouk*,1 and K Ranga Rama Krishnan1

1Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA

Metabolomics, the omics science of biochemistry, is a global approach to understanding regulation of metabolic pathways

and metabolic networks of a biological system. Metabolomics complements data derived from genomics, transcriptomics,

and proteomics to assist in providing a systems approach to the study of human health and disease. In this review we focus

on applications of metabolomics for the study of diseases of the nervous system. We share concepts in metabolomics, tools

used in metabolic profiling and early findings from the study of neuropsychiatric diseases, and drugs used to treat these

diseases. Metabolomics emerges as another powerful tool in central nervous system research.

Neuropsychopharmacology Reviews (2009) 34, 173–186; doi:10.1038/npp.2008.174; published online 8 October 2008

Keywords: metabolomics; pharmacometabolomics; metabonomics; biomarkers; neurodegenerative; psychiatric diseases

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INTRODUCTION

Millions of people suffer from mental illnesses or neurode-generative diseases such as Parkinson’s disease (PD),Alzheimer’s disease, schizophrenia, depression, addiction,autism, dyslexia, and learning disabilities. These, amongother diseases, are in much need for better treatments.Unfortunately, our current understanding of these disordersis limited. In fact it is likely that most of these disorders arenot unitary conditions but may be a conglomeration ofentities that are yet to be defined. Although some progresshas been made in the treatment of psychiatric disordersmany patients do not respond to current therapies. We alsocannot predict who will respond to which treatment. Thisinherent variability coupled with intrinsic differences inpharmacology is the underlying factor that affects ourinability to predict how an individual patient may respondto a selected therapy. Such uncertainty is distressing forpatients and families who engage repeatedly in ‘trial-and-error’ choices in search of ‘the right fit’ and for cliniciansthus resorting to widespread switching of medications(Weiden and Buckley, 2007) and polypharmacy (Tranuliset al, 2008). The inordinately high personal and societalburden of inadequate trial-and-error management under-scores an urgent need for validated biomarkers that

establish diagnosis, guide drug selection, and reliablypredict response to treatment (Insel, 2007; Liebermanet al, 2005).

To understand brain function and its complexity newideas, new approaches, and new technologies are muchneeded. Efforts to scale-up data production and analysis inneuroscience have been escalating. Genomics includingcomparative genomics, gene expression atlases and theorganization of genome-scale projects, gene microarrays,proteomics, monitoring the activity of individual neuronsusing multiple-electrode recordings, and imaging studieshave all provided powerful approaches to the study ofneurological disorders (articles in Nat Neurosci 7, 425,published in 2004). Metabolomics the newest of the omicsapproaches provides powerful tools for defining perturba-tions in metabolic pathways and networks in human disease(Kaddurah-Daouk et al, 2008; Kristal et al, 2007a, b; Lindonet al, 2007; Harrigan and Goodacre, 2003). The metabolomedefines a metabolic state as regulated by net interactionsbetween gene and environment influences and providesinformation that can possibly bridge the gap betweengenotype and phenotype. It provides a missing piece to asystems approach to the study of diseases of the centralnervous system (CNS). Metabolic signatures for CNSdisorders could result in the identification of biomarkersfor disease, for disease progression or for response totherapy. In addition metabolomics provides powerful toolsfor the process of drug discovery and drug development byproviding detailed biochemical knowledge about drugcandidates, their mechanism of action, therapeutic poten-tial, and side effects as exemplified in this review.

Received 30 July 2008; revised 31 August 2008; accepted 31 August2008

*Correspondence: Dr R Kaddurah-Daouk, Department of Psychiatry andBehavioral Sciences, Duke University Medical Center, DUMC 3903,Durham, NC 27710, USA, Tel: + 919-684-2611, Fax: + 919-681-7668;E-mail: [email protected]

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FROM THE GENOME TO THE METABOLOMEIN THE STUDY OF CNS DISORDERS

Advances in genomics, the omics field of DNA sequenceanalysis, have accelerated the ability to link human diseasewith its origins in the genome. Examples in the neuros-ciences include the recognition of genetic risk factors forAlzheimer’s disease such as amyloid mutations, APOe4 vsAPOe2 or APOe3 (Strittmatter et al, 1993; Corder et al,1993; Strittmatter et al, 1993); PD synuclein, Parkin, Pink1,DJ-1 (Polymeropoulos et al, 1997; Morris, 2005; Klein et al,2005; Kitada et al, 1998); Huntington’s disease (HD)expanded CAG repeat in the huntingtin gene (Jenkinset al, 2005; Myers et al, 1993; The Huntington’s DiseaseCollaborative Research Group, 1993); amyotrophic lateralsclerosis (ALS) mutations in SOD (Rosen et al, 1993), andthe importance of serotonin transporter variation inrelation to a number of neuropsychiatric syndromes suchas depression, obsessive compulsive disorder, and neuroti-cism (Serretti et al, 2006; Bloch et al, 2008). Advances intranscriptomics and proteomics might impact our ability tounderstand neurological disorders pathogenesis.

One of the major missing pieces of the ‘-omics’ revolutionwas the -omics field of quantitative metabolite analysisknown as metabolomics (with alternate names used such asmetabonomics and metabolic profiling) (Kaddurah-Daouket al, 2008; Kristal et al, 2007a, b; Lindon et al, 2007;Harrigan and Goodacre, 2003). In contrast to classicalbiochemical approaches that focus tightly on singlemetabolites, single metabolic reactions and their kineticproperties, and/or defined sets of linked (ie, precursor/product, intermediary metabolism) reactions and cycles,metabolomics collects quantitative data on a broader seriesof metabolites in an attempt to gain an overall picture orunderstanding of metabolism and/or metabolic shiftsassociated with conditions of interest (Kristal et al,2007a, b).

METABOLOMICS: CONCEPT, TOOLS, ANDPROCESS

Metabolomics tools enable us to study the metabolome, therepertoire of small molecules present in cells and tissue(Harrigan and Goodacre, 2003; Kaddurah-Daouk et al, 2008;Kristal et al, 2007a, b; Lindon et al, 2007). The identities,concentrations, and fluxes of these substances are thefinal product of interactions between gene expression,protein expression, and the cellular environment (Figure 1).Unlike earlier, more rudimentary analytical methods,metabolomics today offers analytical instruments that cansimultaneously quantitate thousands of substances presentin a biological sample of interest and sophisticatedmathematical tools that can find a molecular signal amongstmillions of pieces of data (Kell, 2004).

Metabolomics utilizes instruments that can simulta-neously quantitate thousands of small molecules in abiological sample. This analytical capability must then be

joined to sophisticated mathematical tools that can identifya molecular signal for a disease or a drug. Ideally,metabolomics will ultimately contribute a detailed map ofthe regulation of metabolic pathways, and, therefore, of theinteraction of proteins encoded by the genome withenvironmental factors, including drug exposure. Therefore,the metabolome represents a state function for anindividual at a particular point in time or after exposureto a specific environmental stimulus (eg, a specific drug orpotentially even a mood state). Many diseases disruptmetabolism and result in changes that are long lasting andthat can be captured as metabolic signatures. Initialmetabolomic signatures have already been reported forseveral disease states, including motor neuron disease(MND) (Rozen et al, 2005), depression (Paige et al, 2007),schizophrenia (Holmes et al, 2006; Kaddurah-Daouk,2006; Kaddurah-Daouk et al, 2007), Alzheimer’s disease(Han et al, 2002), cardiovascular and coronary arterydisease (Brindle et al, 2002; Sabatine et al, 2005), hyperten-sion (Brindle et al, 2003), subarachnoid hemorrhage(Dunne et al, 2005), preeclampsia (Crocker et al, 2005),type 2 diabetes (Van der Greef et al, 2007; Van Doorn et al,2007; Wang et al, 2005; Yuan et al, 2007), liver cancer(Yang et al, 2004), ovarian cancer (Odunsi et al, 2005),breast cancer (Fan et al, 2005), and HD (Underwood et al,2006). These signatures are made up of tens of metabolitesthat are deregulated, with concentrations that are modifiedin the disease state or after drug exposure. As a result,analysis of these signatures and their components canpotentially provide information with regard to diseasepathophysiology.

The choice of metabolomic analytical instrumentationand software is generally goal-specific, as each type ofinstrument has certain strengths. Liquid chromatographyfollowed by coulometric array detection, for example, hasbeen used in the identification of signatures in ALS (Rozen

Genome

Transcriptome

Proteome

Metabolome

Environm

ent

(Drug exposure)

Healthstate

Diseasestate

Figure 1. Flow of information from the genetic code (DNA) to proteins,and finally to metabolites. The environment and DNA affect the endproducts and influence health disease states. In this case drug exposureis the environmental alteration being tested.

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et al, 2005) and most recently for PD (Bogdanov et al, 2008).It is excellent for mapping neurotransmitter pathways (eg,dopamine (DA) and serotonin) and pathways involved inoxidative stress. Gas chromatography in conjunction withmass spectroscopy is often used in the analysis of lipidsubsets (Kaddurah-Daouk et al, 2007; Watkins, 2004;Watson, 2006). Liquid chromatography with mass spectro-scopy is often used to obtain the largest possiblebiochemical profile information subset. It is a sensitive toolthat can be used to characterize, identify, and quantify alarge number of compounds in a biological sample (Kristalet al, 2007a, b; Fiehn, 2008; Tolstikov et al, 2007) wheremetabolite concentrations might cover a broad range ofinformation with regard to disease pathophysiology. Inaddition to popular high-sample-throughput applications,NMR is particularly powerful for metabolite structuraldeterminations, including the atomic positions of isotopiclabels (eg, 13C, 15N, or 2H) in different isotopomersgenerated during stable isotope tracer studies (Fan andLane, 2007). For example NMR-based high throughputanalysis has been used successfully in toxicology studies(Coen et al, 2004; Lindon et al, 2000, 2003). NMRapplications provide detailed maps of biochemical pathwaysor networks, which can also serve as crucial inputs for insilico quantitative flux analysis (Dauner et al, 2001; de Graafet al, 2000).

An overview of a ‘typical’ metabolomics study is depictedin Kaddurah-Daouk et al (2008). Samples of interest (eg,plasma, cerebral spinal fluid, tissue biopsy, etc) arecollected, then small molecules are extracted from thesample and are analyzed using techniques that separate andquantitate molecules of interest as mentioned above.Combinations of these techniques can be used to augmentseparations and/or expand the analyte information col-lected. These data sets must then be collected and curated, aprocess that can take significant time for the overallexperiment yet tools are being developed to make thisprocess faster (Styczynski et al, 2007). After curation, thedata are analyzed by one or more software packagesdesigned for studies of data sets that are far too large forhuman evaluation. A database is then generated for thediseased patients and another for the controls or for thesame patients before and after drug therapy. Thesedatabases include levels of detectable metabolites andidentity (or a description of the properties) of themetabolites (ie, oxidation–reduction potential, mass/chargeratio, etc, whenever known.)

The application of software tools for the analysis of theinformation contained in a database can: (1) identifysignature of a disease (eg, compounds that highlight adisease state); (2) predict class (eg, disease or control, pre-or postdrug exposure); (3) identify unrecognized groups inthe data (eg, drug response subgroups); (4) identifyinteractions between variables; and (5) place these variableswithin known biochemical pathways.

Metabolomics data can be analyzed with a range ofstatistical and machine-learning algorithms. These algo-

rithms can be classified within two major classes: unsu-pervised and supervised (Kell, 2004). They can be useful inthe identification of biomarkers (Sajda, 2006; Shin andMarkey, 2006). Unsupervised algorithms find patterns inthe data without any biases, and are typically driven by thelargest changes (variance). Supervised algorithms requirethat samples be labeled in groups a priori, and they uncoverthe features (variables) that best discriminate between thosegroups. Examples of unsupervised methods that have beenroutinely used in analyzing molecular fingerprinting datainclude principal component analysis (PCA) and self-organizing maps (Patterson et al, 2008).

METABOLIC CHANGES IN CNS DISORDERS:RATIONALE FOR METABOLOMICSAPPROACH

Over the last half a century, neurochemists have identified aseries of molecules in the brain which carry importantmessenger functions such as DA, epinephrine, norepinephr-ine (NE), serotonin, acetylcholine (Ach), g-amino butyricacid (GABA), substance P, bradykinin, and many more. Theidentification of these molecules required technicallychallenging experiments and once these compounds wereidentified, research shifted to understanding their mechan-isms of regulation, synthesis and release, and the pathwaysthat gave rise to them. Notably, however, these aremolecules that are primarily thought by most, even today,as individual molecules. The next stageFthe considerationof pathwaysFentered the picture as people asked howmolecules were made, and what the limiting factors in theirsynthesis were. Today, metabolomics provides new andpowerful tools to study hundreds of these key moleculessimultaneously and quantitatively (Kristal et al, 2007a, b).We can now interrogate almost to entirety metaboliteswithin key neurotransmitter pathways such as DA andserotonin enabling us to move to a far more detailedanalysis of implications of these pathways in CNS disordersand for mapping more clearly mechanism of action of drugsthat target these pathways.

Abnormalities in several metabolic pathways have beenidentified in neuropsychiatric disorders. There is increasingevidence linking mitochondrial dysfunction, as well asmetabolic abnormalities to neurodegenerative diseases(Thomas and Beal, 2007; Lin and Beal, 2006). Mitochondriaare critical regulators of cell death, a key feature ofneurodegeneration. There may be a variety of factors, whichlink mitochondria to neurodegeneration in the variousneurodegenerative diseases. For instance, in HD there isrecent evidence that suggests that the coactivator PGC1-a, akey regulator of mitochondria biogenesis and respiration, isdownregulated. This appears to be the case both in patients,as well as several animal models of HD. In PD the autosomalrecessive genes Parkin, DJ1, and PINK1 are all linked toeither oxidative stress or mitochondrial dysfunction. In ALSthere is strong evidence that mutant superoxide dismutase

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directly interacts with the outer mitochondrial membrane, aswell as the intermembrane space and matrix. A number oftherapies that target basic mitochondrial processes such asenergy metabolism and free radical generation are underdevelopment.

In psychiatric diseases many metabolic changes havebeen identified and linked to disease pathogenesis. Changesin neurotransmitter pathways such as DA and serotoninseem to be important in disease pathogenesis and responseto therapy.

In schizophrenia impairments in neurotransmitter, signaltransduction, antioxidant system, membrane composition,and immune functions have been noted among severalother changes (Mahadik and Yao, 2006). There is sufficientevidence to suggest that aberrant neurotransmission mightplay a role in the pathogenesis of schizophrenia. Inparticular, aberrant dopaminergic, serotonergic, and gluta-matergic systems have been noted (Miyamoto et al, 2003).Some data also exist that support the involvement ofhistamine, and Ach neurotransmitter systems (Feuerstein,2008). It is unclear, however, to what extent theseneurochemical findings reflect primary rather than second-ary pathology, compensatory mechanisms, or environmen-tal influences, and it is not clear how these pathwaysinteract in schizophrenia pathogenesis. Evidence exists thatphospholipids which play a critical role in the structure andfunction of membranes are impaired in schizophrenia(Horrobin, 1998; Berger et al, 2002; Skosnik and Yao,2003; Mahadik and Yao, 2006). Lipids and their constituentfatty acids which provide scaffolding for many keyfunctional systems, including neurotransmitter receptorbinding, signal transduction, transmembrane ion channels,prostanoid synthesis, and mitochondrial electron-transportsystems could contribute to pathogenesis in schizophrenia(Rotrosen and Wolkin, 1987; Lieberman and Koreen, 1993).

In bipolar disorder (BD) perturbations in neurotransmit-ters systems including Ach (Janowsky et al, 1972), GABA(Brambilla et al, 2003), norepinephrine (NE) (Schildkraut,1974), DA (Goodwin and Sack, 1974), glutamate (Glu)(Kugaya and Sanacora, 2005), and serotonin (5HT)(Mahmood and Silverstone, 2001; Sobczak et al, 2002) havebeen identified. Abnormalities in lipids and increasedinflammation and immune activation may also contributeto the pathogenesis of mood disorders, in part bymodulating the metabolism of NT systems (Wichers et al,2005). It is not well understood which biochemical pathwaysare regulated during the shift from the manic to thedepressed states.

In major depressive disorders (MDD) multiple neurotrans-mitter systems are thought to be deregulated and muchattention has been given to the role of impairment in centralmonoaminergic function. Given that SSRIs are commonlyused for the treatment of MDD, serotonin (5HT) abnormitieshave been traditionally linked to MDD’ pathogenesis (Delgadoand Moreno, 2000). Not only serotonergic, but alsonoradrenergic systems are likely to be involved in antide-pressant action, and specific impairments in these systems

may also play a role in disease pathogenesis (Delgado andMoreno, 2000). The GABAergic neurotransmitter system hasbecome increasingly implicated in the neurobiology of mooddisorders (Brambilla et al, 2003; Sanacora and Saricicek,2007). GABAergic involvement in the pathophysiology andtreatment of mood disorders is supported by several lines ofevidence spanning from animal studies showing stress-relatedchanges in GABAergic function to the demonstration ofGABAergic abnormalities and genetic associations in de-pressed patients (Brambilla et al, 2003; Sanacora andSaricicek, 2007). Specifically, the association of lower GABAlevels with depression is a consistent finding (Petty andSchlesser, 1981). Indeed, reduced levels have been consistentlyreported in plasma (Petty and Schlesser, 1981), CSF (Gerneret al, 1984; Kasa et al, 1982), and brain (Bhagwagar et al, 2007;Hasler et al, 2007; Sanacora and Saricicek, 2007) ofindividuals diagnosed with depression. Likewise, GABAagonists and antagonists have the ability to modulatebehavioral models of depression in rodents, existing anti-depressant medications have GABAergic effects and there issome evidence for a clinical antidepressant efficacy associatedwith GABAergic drugs. Glutamatergic abnormalities in MDDpatients including decreased CSF Glu and glycine (Frye et al,2007) but increased levels of Gln have been reported (Levineet al, 2000). Furthermore, antidepressant and mood stabiliz-ing medications have glutamatergic effects and Glu modulat-ing agents in the treatment of depression.

APPLICATIONS OF METABOLOMICS IN THESTUDY OF CNS DISEASES

It is clear that perturbations in a variety of metabolic andsignaling pathways could contribute to the pathogenesis ofCNS disorders. Metabolomics provides powerful tools tomap in greater detail these perturbations and their relation-ship to disease pathogenesis and response to therapy.Applications of metabolomics for the study of CNS diseasesincludes: (1) added information about mechanisms ofdisease; (2) identification of prognostic, diagnostic, andsurrogate markers for a disease state; (3) the ability tosubclassify disease based on metabolic profiles; (4) identi-fication biomarkers for drug response phenotypes and fordevelopment of metabolic side effects (pharmacometabo-lomics); and (5) added tools in the process of drugdiscovery and drug development.

Psychiatry and neurology have long posed uniquedifficulties for the development of biomarkers because ofthe lack of access to relevant tissues. Initial biomarkersstudies have relied on studies of platelet markers, spinalfluid metabolites, tissue obtained at autopsy from suicidevictims or those who died of natural causes, as well as use ofimaging techniques ranging from EEG, CT, MRI, MRS, andPET scans. All of these techniques have limitationsespecially given that our disease classification systems havealso evolved and matured over the years. Although spinalfluid studies of numerous metabolites have been conducted

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in the study of CNS diseases, to date there exists nostandard ‘normative’ reference level for most of thesemetabolites. This is because individual studies couldmeasure only a few metabolites at a time and measurementsare usually performed only in diseased individuals andrelatively few controls are studied. Thus, there is a widevariation from study to study in the ‘normal’ values.Ventricular CSF and lumber CSF metabolites are notidentical and it is not totally clear which correlates betterwith brain biochemistry. The correlations between plasmaand CSF have also not been well established for many ofthese metabolites. In addition, the high complexity of thebrain (compared to say the muscle or the heart) has madethe identification of reliable biomarkers much moredifficult. Thus, these issues argue for a systematic correla-tive study of plasma and spinal fluid metabolites using atechnology such as metabolomics that can provide assess-ment of global metabolite signatures. Further, the establish-ment of ‘brain banks’ with rapid autopsy procedures atmany centers has now made available tissue samples thatcan be analyzed in disorders such as Alzheimer’s, majordepression, schizophrenia, Lewy body dementia, fronto-temporal dementia, and other conditions. Many psychiatricclinical trials also routinely collect samples from well-characterized patients before and after treatment with drugsor placebo. It is of interest to note that metabolomics canprovide means to study variation in biochemical profilesamong patients and could result in more effective ways tosub classify patients and provide well-defined intermediatephenotype that can describe clinical phenotype beingobserved by clinicians. This could have a significant impacton clinical trials design and outcome as we can start toevaluate how subpopulations of patients respond to therapyand select drugs that are more effective for each grouping.The metabolic signatures of antidepressants (SSRI class) arebeing mapped in good and poor responders (MetabolomicsNetwork for Drug Response Phenotypes funded by NIH) toobtain information that can lead to a deeper understandingof pathways implicated in response and variation inresponse to these therapies. Mapping of signatures asso-ciated with response and of signatures associated withdevelopment of metabolic adverse events can lead todevelopment of useful biomarkers that can help select theright drug for each patients resulting in personalizingtherapy. Indeed, a new field is emerging which is calledPharmacometabolomis (of Pharmacometabonomics) thancan compliment the field of pharmacogenomics.

Signatures of disease can also provide clues for drugtargets. For example if a particular signature is related to adisorder then one could trace back potential targets in thatmetabolic framework for drug development. In addition asignature for a CNS disorder that suggests that certainmetabolites are low or missing, may suggest potentialways of replacing or altering production of these metabo-lites as modes of treatment in line with replacement of DAin PD.

EARLY FINDINGS FROM METABOLOMICSSTUDIES OF CNS DISORDERS

We have started to explore metabolic perturbations inneurodegenerative and psychiatric diseases. We attempt tounderstand unique and commonly perturbed pathways thatcontribute to the death of neurons in degenerative diseasessuch as ALS, PD, and HD. In psychiatric disorders such asdepression, bipolar and schizophrenia we attempt toidentify biomarkers for disease, disease progression, andresponse to therapy and define pathways implicated. Belowwe summarize initial findings from metabolomics studies ofsuch CNS disorders.

METABOLOMIC ANALYSIS OFNEURODEGENERATIVE DISEASES

Motor Neuron Disease

MNDs are a heterogeneous group of rare disordersthat affect motor neurons and cause diverse signs andsymptoms. These are fatal diseases that are poorly under-stood and treatments are mostly nonexistent or supportiveat best.

ALS affects both lower and upper motor neurons (Row-land and Shneider, 2001). Whether ALS is a single diseaseentity or a syndrome caused by different conditions remainsunknown. In addition, it is not clear whether MNDs areclosely related but distinct disorders or whether theyrepresent different points on the spectrum of a singledisease.

We hypothesized that diseases such as ALS mightproduce characteristic perturbations of the metabolome.We used HPLC followed by electrochemical detection toprofile plasma from 28 patients with MND and 30 healthycontrols (Rozen et al, 2005). Using multivariate regressiontechniques we were able to distinguish ALS patients fromcontrols (Figures 2 and 3) and also separate ALS patients onRiluzole or off Riluzole. Please note that in Figure 2 leftpanel is control subjects whereas central panels and rightpanel are patients. Each column is a subject and each raw isa metabolite. Red are metabolites that are upregulated andblue downregulated. A sub-signature for patients withLMND and slow disease progression has been identified(Figure 2, right panel of four patients). In addition, asignature for the drug riluzole (used to treat patients withALS) that includes endogenously induced metabolites hasbeen defined (Figure 2, panel labeled MND and riluzole). Ina subsequent study of 19 subjects with MND who were nottaking riluzole and 33 healthy control subjects, sixcompounds were significantly elevated in MND, whereasthe number of compounds with decreased concentrationwas similar to the initial study (Rozen et al, 2005).

In both data sets, it was possible to separate MNDpatients from controls using multivariate regression tech-niques. These data suggested that metabolomic studies canbe used to detect signatures for some CNS disorders

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peripherally, could identify sub-groupings within a diseasestate, and that they can also detect drug exposuresignatures. Elucidation of the structures of signature

molecules in ALS and other forms of MND might provideinsight into aberrant biochemical pathways that could leadto diagnostic markers and targets for drug design. Aprogram funded by the ALS Association (collaborationbetween MGH, Metabolon Inc. and network of academicinstitutions) has been initiated to collect samples fromMND patients nationally to better dissect MNDs using avariety of metabolomics platforms including LC–MS andgas chromatography–mass spectrometry (GC–MS). We aredefining metabolic signatures for UMNDs, LMNDs, andALS to determine the relatedness of these diseases at themetabolic level and to define biochemical markers fordisease and progression. Although clinicians can classifythese diseases in a rough way, we believe that metabolomicscan provide a more objective classification.

Metabolomic Analysis of Parkinson’s Disease

A recent study was conducted by Bogdanov et al (2008) tolook for biomarkers in plasma useful for the diagnosis ofPD. Metabolomic profiling using high-performance liquidchromatography coupled with electrochemical coulometricarray detection was used to evaluate 25 controls and 66 PDpatients. Initial analysis of the metabolomic profiles fromplasma demonstrated a clear differentiation between PDpatients and control subjects (Po0.01 by permutation test).A partial least squares discriminant analysis (PLS-DA)scores plot based on complete digital maps is shown inFigure 4. Both unmedicated PD patients and the patients ondifferent types of antiparkinsonian medication (Sinemet,

Controls

Com

poun

dsSamples

MND noriluzole MND and riluzole

Relative class association:Standard deviations frommean compound concentration

Actual compound

All compoundsshown aresignificantlyhigher in MND, P < 0.05

P = 0.05

*

2.5 1.5 0.5 0.5 1.5 2.5

0.2 0.4 0.6 0.8 1.0

Figure 2. Metabolites with significantly different concentrations in normal and motor neuron disease (MND) plasmas in the first study. (a) Metaboliteshigher in MND patients than in controls. Each row represents a metabolite, each column represents a healthy control or a patient, and each coloredsquare represents the relative concentration of a single metabolite in a single person. Compounds are sorted by decreasing association with MND.Significant association measures at P¼ 0.05 are indicated by black dots at the right. The association measures for the actual data are indicated by reddots. The metabolites that are high in MND define three subgroups. These consist of patients not taking riluzole, patients taking riluzole, and four patientswith a distinctive signature (indicated by an asterisk). Three of these patients had LMN disease. Figure presented with permission from MetabolomicsJournal. Rozen et al, 2005.

Control

MND no riluzole

MND and riluzole

Atypical MND

Figure 3. Partial least squares discriminant analysis (PLS-DA) distinguishedsubgroups of motor neuron disease (MND). Models using projections intothree dimensions provided statistically significant separations betweensubgroups (Po0.01 by permutation testFrandom assignment ofsamples to subgroups). Red are controls; purple are MND patients onriluzole; blue are MND patients off riluzole and black are atypical MNDpatients. Figure presented with permission from Metabolomics Journal.Rozen et al, 2005.

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DA receptor agonists, and combination of both Sinemet andthe agonists) were included in the initial analysis. Toevaluate the possibility that the observed separationbetween the patients and controls is due to medicationsthat patients are on and not due to disease state plasmafrom 15 unmedicated PD patients were profiled andcompared to profiles of 25 control subjects. A PLS-DAscores plot based on the analysis of complete digital mapsfor unmedicated PD patients and control subjects showed acomplete and significant separation of these two groups(Po0.01 by permutation test; Figure 5). The metabolites 8-OHdG and glutathione were significantly increased in PDpatients whereas the level of uric acid was significantlyreduced. Both glutathione and uric acid are well-knownantioxidants. Higher uric acid levels lower risk for PD andslow the progression of the illness (de Lau et al, 2005;Ascherio et al, 2006). Glutathione levels are reduced in thesubstantia nigra of PD postmortem brain tissue. Increasedoxidized glutathione was found in plasma of PD patients(Younes-Mhenni et al, 2007). Changes in levels and ratio ofoxidized to reduced glutathione probably reflect a responseto oxidative damage.

It is of interest to note that drugs used to treat PD such asSinemet, DA receptor agonists, and combination of bothSinemet and the agonists do induce partial reversal ofmetabolic profiles in PD patients (Bogdanov et al, 2008)where they become more similar to controls. Few patients’metabolic profiles did not revert and it is of interest to try tocorrelate in the future reversal of metabolic profiles withreversal of clinical symptoms.

This PD study demonstrates the promise of metabolomicsin the development of biomarkers for PD. Diagnosticmarkers could have great utility in accelerating clinicaltrials and might enable sub-stratification of patients andidentification of homogenous population of PD subjects not

contaminated by other Parkinsonian syndromes. A diseaseprogression marker if identified and validated in long-itudinal studies could serve as a surrogate endpoint, whichmight be more quantitative than clinical scales. Lastly, ifone could identify early biochemical changes and signaturesthat are unique to patients at the earliest stages of illness, oreven pre-symptomatically it would be of great importance.One could then screen first-degree relatives, patients withhyposmia and patients carrying genetic risk factors. Anumber of other manifestations of systemic illness mayincrease risk for PD. If these patients can be identified earlyone could carry out primary prevention trials.

Metabolomics in the Study of Huntington’sDisease

HD is a devastating autosomal-dominant neurodegenerativecondition that manifests with movement disorder, beha-vioral disturbance, and cognitive deterioration. Although itcan present at any age, the median age of onset is 40 years,and death typically follows some 15–20 years after symptomonset. The HD mutation is a (CAG)n trinucleotide repeatexpansion at the 50 end of the transcript encoding HD. The(CAG)n repeats are translated into a polyglutamine tract.Disease is caused by 435 CAG repeats and the age at onsetcorrelates inversely with CAG repeat number. Considerableprogress has been made with modeling Huntington’spathogenesis in cell and animal models. The developmentof biomarkers that better sub classify patients or thatcorrelate with disease and disease progression couldprovide very valuable tools as clinical trials are costly andtime consuming due to the slow disease course, insidiousonset and patient-to-patient variability. The validation ofanimal models which are used to select candidate therapiesand an evaluation of their relevance to human disease is of

t[2]

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Figure 4. Partial least squares discriminant analysis (PLS-DA) scoresplot showing a significant separation (Po0.01 by permutation testFran-dom assignment of samples to different groups) between control subjects(n¼ 25) and all Parkinson’s disease (PD) patients (medicated andunmedicated, n¼66) using complete digital maps. Figure presentedwith permission from Brain. Bogdanov et al, 2008.

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Figure 5. Partial least squares discriminant analysis (PLS-DA) scoresplot showing a significant separation (Po0.01 by permutation test)between control subjects (n¼25) and unmedicated PD patients (n¼ 15)using complete digital maps. Figure presented with permission fromBrain. Bogdanov et al, 2008.

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great importance as they could yield models that are moreeffective in predicting therapeutic outcome in patients.

In a study conducted by Underwood et al (2006)metabolic profiling of human HD patients and a transgenicmouse model for HD by gas chromatography–time-of-flight–mass spectrometry (GC–TOF–MS) revealed cleardifferences in metabolic profiles. This trend was seen inboth transgenic mice and wild-type littermates, and humanpatients compared to control subjects. The metabolicsignatures in mice and humans are indicative of a changeto a procatabolic phenotype in early HD precedingsymptom onset, with changes in various markers of fattyacid breakdown (including glycerol and malonate) and alsoin certain aliphatic amino acids. The data raise the prospectof a robust molecular definition of progression of HD beforesymptom onset, and if validated in a genuinely prospectivefashion these biomarker trajectories could facilitate thedevelopment of useful therapies for this disease as well asbiomarkers for disease and its progression.

Metabolomic Analysis of a Mouse Model ofBatten Disease

Neuronal ceroid lipofuscinoses (NCLs) are a series ofautosomal recessive diseases that collectively constitute themost common cause of childhood neurodegeneration withan incidence of 1 in 12 500 (Goebel and Sharp, 1998;Banerjee et al, 1992). The disorders are typified by theirprogressive nature with symptoms including visual dis-turbances, psychomotor deterioration, mental impairment,worsening seizures, blindness, and, ultimately, prematuredeath (Gardiner, 2002; Hofmann et al, 2002; Mitchison et al,2004; Mitchison and Mole, 2001; Wisniewski et al, 2001).Although six of the causative genes have been characterized,the underlying disease pathogenesis for this family ofdisorders is unknown. The Cln3 gene has been identified asresponsible for the juvenile NCL (Batten disease) and ananimal model has been generated for this disease. Using ametabolomics approach based on high resolution 1H NMRspectroscopy of the cortex, cerebellum, and remainingregions of the brain in conjunction with statistical patternrecognition, Pears et al (2005) evaluated metabolic deficitsin Cln3 null mutant mice aged 1–6 months. They definedincreased Glu concentration and a decrease in GABAconcentration in aqueous extracts from the three regionsof the brain. These changes are consistent with the reportedaltered expression of genes involved in Glu metabolism inolder mice and imply a change in neurotransmitter cyclingbetween Glu/glutamine and the production of GABA.Further variations in myo-inositol, creatine, and N-acetyl-aspartate were also identified. These metabolic changeswere distinct from the normal aging/developmentalprocess. Together, these changes represent the firstdocumented presymptomatic symptoms of the Cln3 mouseat 1 month of age and demonstrate the versatility of 1HNMR spectroscopy as a tool for phenotyping mouse modelsof disease.

METABOLOMIC ANALYSIS IN PSYCHIATRICDISORDERS

We have been using random and targeted metabolomicsapproaches for the study of psychiatric disorders. We aretesting existing hypothesis around the impairment of neuro-transmitter pathways such as DA and serotonin and lipidpathways and we are also generating new hypothesis aboutpathways that might be implicated in disease and diseaseprogression. Below we provide early findings for such studies.

Metabolic Signatures in Depression

Given the body of evidence suggesting that abnormalities inthe metabolome do exist both centrally and in the peripherya recently published metabolomics study was conducted inplasma as a first step towards mapping biomarkers fordepression. Metabolomic analysis of blood plasma wasperformed on 9 depressed, 11 remitted, and 10 never-depressed older adults (Paige et al, 2007). Hundreds ofmetabolites were measured using GC–MS. Metaboliteidentification was based on a combination of chromato-graphic properties and mass spectra. A library of 800commercially available human metabolite standards (Me-tabolon Inc.) has been assembled and analyzed on an MSplatform that helped in compound identification.

Concentrations of each metabolite were normalized to meanscaled values of two recovery standards that representhydrophobic and hydrophilic compounds. Univariate t-tests(Welch’s two-sample t-tests) were performed for eachmetabolite to determine metabolic differences between healthycontrols, and individuals who are depressed or in remission.False discovery rate (FDR) was accounted for and the multipletesting and q-value were estimated FDR for every possible listof ‘significant’ metabolites. Metabolites that were altered incurrently depressed patients when compared with controlsincluded several fatty acids, glycerol, and GABA. Analysescomparing concentrations in remitted and currently depressedpatients revealed a pattern of metabolite alterations similar tothe control vs currently depressed analyses. One differenceobserved in the remitted patients relative to the depressedpatients was elevation of the concentration of the ketone 3-hydroxybutanoic acid (Figure 6).

These preliminary results will need to be examined andvalidated in larger longitudinal cohorts. However, thesefindings suggest that the depressed state may be associatedwith alterations in the metabolism of lipids and neuro-transmitters, and that treatment with antidepressantsadjusts some of the aberrant pathways in disease so thatthe patients in remission have a metabolic profile moresimilar to controls than to the depressed population. Anevaluation of such changes in CSF samples is needed toestablish how closely these findings are to central changes.

Metabolic Signatures in Schizophrenia and ItsTreatment

Several metabolomics studies have recently been conductedin an attempt to better define pathways modified in

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schizophrenia and its treatment (Holmes et al, 2006;Kaddurah-Daouk et al, 2007; Tkachev et al, 2007; Tsanget al, 2006). In one study we used a specialized lipidomicsplatform and measured more than 300 polar and nonpolarlipid metabolites (structural and energetic lipids) across 7lipid classes to evaluate global lipid changes in schizo-phrenia before and after treatment with three commonlyprescribed atypical antipsychotics, olanzapine, risperidone,and aripiprazole (Kaddurah-Daouk et al, 2007).

Lipidomics is a branch of metabolomics that specificallyfocuses on comprehensive assessment of lipid biochemistry(German et al, 2007; Watson, 2006; Wiest and Watkins,2007). In this particular study, lipid profiles were obtainedfor 50 patients with schizophrenia before and after 2–3weeks of treatment with olanzapine, risperidone, oraripiprazole. Data are presented in heat maps where columnheaders indicate fatty acid metabolites as they appear ineach distinct lipid class (rows). Metabolites that areupregulated are shown in red whereas those that aredownregulated are shown in green. Figure 7a showsdifferences in individual lipid metabolites in the plasma ofpatients with schizophrenia as compared to controlswhereas Figure 7b shows effect of three drugs olanzapine,risperidone, and aripiprazole. Figure 7c shows the mostsignificantly modified lipid metabolites in plasma ofpatients treated with olanzapine and highlights which ofthese metabolites are also modified upon treatment withrisperidone or aripiprazole.

At baseline, and before drug treatment, major changeswere noted in two phospholipids classes, phosphotidyletha-nolamine (PE) and phosphotidylcholine (PC), suggestingthat phospholipids that are important in proper membrane

structure and function seem to be impaired in patients withschizophrenia (Figure 7; Kaddurah-Daouk et al, 2007). Thisconfirmed previous observations but establishes a far moredetailed biochemical map for sites of perturbations. Some ofthe biochemical perturbations were seen within the o-3 ando-6 subclasses in PE and PC. In addition shifts betweensaturated and ployunsaturated fatty acids were noted(Figure 7; Kaddurah-Daouk et al, 2007).

The effects of three antipsychotic drugs, olanzapine,risperidone, and aripiprazole, on lipid biochemical path-ways were then evaluated by comparing metabolic profilesat baseline to post-treatment (Kaddurah-Daouk et al, 2007).It was of interest that each of the three drugs studied had aunique signature suggesting that although these drugs sharesome effects, they also have many effects that are unique foreach. PE concentrations that were suppressed at baseline inpatients with schizophrenia were elevated after treatmentwith all three drugs. However, olanzapine and risperidoneaffected a much broader range of lipid classes than didaripiprazole, with approximately 50 lipids that wereincreased after exposure to these drugs, but not afteraripiprazole therapy (Figure 7). On balance, aripiprazoleinduced minimal changes in the lipidome (Figure 7),consistent with its limited metabolic side effects. Therewere also increased concentrations of triacylglycerols anddecreased free fatty acid concentrations after both olanza-pine and risperidone, but not after aripiprazole therapy(Figure 7; Kaddurah-Daouk et al, 2007). All of these changessuggest peripheral effects that might be related to themetabolic side effects that have been reported for this classof drugs and highlights lipases in the liver as possiblytargets for these drugs. Finally, a PCA identified baseline

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Figure 6. Metabolites significantly different in the depressed state when compared to remitted state or healthy nondepressed controls. Figurepresented with permission from Int J Geriatric Psychiatry. Paige et al, 2007.

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lipid alterations that seemed to correlate with acutetreatment response (Kaddurah-Daouk et al, 2007).

Collectively, these results raised the possibility that amore definitive long-term randomized study of these drugs

in which global lipid changes would be correlated withclinical outcomes might yield biomarkers related toresponse and development of side effects. This study ofatypical antipsychotic drugs illustrates the way in which

Figure 7. Heat map showing differences in individual lipid metabolites in the plasma of patients with schizophrenia as compared to controls (a), in theplasma of schizophrenic patients post-treatment as compared to pretreatment (b) with olanzapine, risperidone, and aripiprazole. (c) The mostsignificantly modified lipid metabolites in plasma of patients treated with olanzapine and highlights which of these metabolites are also modified upontreatment with risperidone or aripiprazole. The column headers indicate fatty acid metabolites as they appear in each distinct lipid class (rows). In (a),lipids whose percent levels were higher in patients vs controls are shown in red whereas those with decreased level are shown in green. In (b) and (c), thepercent increase in any lipid upon treatment with drug is shown in red squares and decrease in green squares. The brightness of each colorcorresponded to the magnitude of the difference in quartiles. The brighter the square is the larger the difference. Figure presented with permission fromMolecular Psychiatry. Kaddurah-Daouk et al, 2007.

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metabolomics might contribute to our understanding ofdrug response phenotypes and how it provides tools toanalyze pathways implicated in variation to response forthis class of drugs. In addition it illustrates how metabo-lomics could be a very valuable tool for biomarkerdiscovery. Studies carried by our newly established Schizo-phrenia Metabolomics Network are generating data aboutglobal changes in neurotransmitter pathways, purine path-ways, one carbon metabolism-related pathways, intermedi-ary metabolic pathways, and other pathways and willprovide a far better chance at defining a truly comprehen-sive signature for schizophrenia.

Additional metabolomics studies in schizophrenia in-clude a (1)H NMR spectroscopy-based metabonomicanalysis plasma where samples from 21 pairs of mono-zygotic twins discordant for schizophrenia and 8 pairs ofage-matched healthy twins demonstrated alterations in lipidprofiles of both affected and unaffected schizophrenia twins(Tsang et al, 2006). In another study (1)H NMR profiling ofCSF samples from drug-naıve patients with first-onsetschizophrenia, suggested alterations in glucose regulationan abnormality that seems to get corrected by earlytreatment with antipsychotics (Holmes et al, 2006). Finally,an interesting metabolomic study on post-mortem tissueprovides support to the notion that abnormalities at thelevel of glutamatergic neurotransmission and myelinsynthesis may be important in schizophrenia (Tkachevet al, 2007).

Metabolic Signatures in Bipolar Disease

Bipolar affective disorder (BD) is a severe and debilitatingpsychiatric condition characterized by the alternating moodstates of mania and depression. The pathophysiology of thedisorder and the mechanism of action of therapies used forits treatment remain poorly understood. Lan et al, 2008used 1H NMR spectroscopy-based metabonomic analysis toidentify molecular changes in post-mortem brain tissue(dorsolateral prefrontal cortex) of patients with a history ofBD. In an animal model they also evaluated signatures oflithium and valproate two drugs used to treat BD. Severalmetabolites were found to be concordantly altered in boththe animal and human tissues. Glu levels were increased inpost-mortem bipolar brain, whereas the Glu/glutamine ratiowas decreased following valproate treatment, and GABAlevels were increased after lithium treatment, suggestingthat the balance of excitatory/inhibitory neurotransmissionis central to the disorder.

In another study and using GC–TOF–MS we measured246 metabolite signals in plasma of patients with BD preand post-treatment with lithium. Using 89 metabolitesidentified with chemical structures we were able to achieveclear separation between pre- and post-treatment samplesusing PCA and PLS plots (RKD). Treatment was associatedwith a significant change in levels of 20 metabolites. Some ofthese metabolites included molecules belonging to pathwayspostulated to be responsible for lithium’s therapeutic effects

and some were previously unidentified (unpublished data).We are trying to define which sets of metabolites at baselinecorrelate with positive therapeutic outcome.

Collectively, data demonstrate the high potential ofmetabolomics for the identification of biomarkers andmetabolic signatures of treatment response. Replicationstudies are underway to validate our findings.

Metabolomics in Addictive Disorder

Many experiments are underway to evaluate signatures inaddicts who use cocaine or opioids. Early findings suggestthat neurotransmitter pathways, purine pathways, andpathway implicated in oxidative stress all seem to beaffected by these addictive substances. Further validation ofthese findings could provide new insights about addictionmechanisms and ways to try to intervene with that.

FUTURE DIRECTIONS AND CLINICALIMPLICATIONS

The study of metabolism at the global or ‘-omics’ level,referred to as metabolomics, is a new but rapidly growingfield that has the potential to impact our understanding ofmolecular mechanisms of disease. Disease states likeschizophrenia disrupt metabolism and result in metabolicsignatures that are long lasting and that can be definedusing complimentary metabolomics platforms. A deeperunderstanding of global perturbations in biochemicalpathways in complex diseases such as schizophrenia,depression, Parkinson’s, and Alzheimer’s disease and upontreatment with drugs could provide valuable insights aboutmechanisms of disease, drug effects, and variation in drugresponse and provide needed prognostic, diagnostic andsurrogate biomarkers. In addition, metabolomics couldprovide biochemical labels to the diverse clinical manifesta-tions of CNS diseases leading to sub-classification of diseasebased on different etiologies and biochemical perturbations.This could stream line clinical trials and improve outcomes.It should be noted that research at this stage represents firststeps towards the development of a metabolic signature asbiomarkers for disease or its treatment. We are still learninghow to deal with confounding factors and our sample sizesso far are relatively small. Proper matching of patients andcontrols for age gender ethnic background and many otherfactors should be considered carefully. Close monitoring ofdiet and exercise and possible confounding effects of othermedications and disease states have to be considered.Ideally more than one site should be used for recruitment ofpatients and controls. Longitudinal studies are needed toconfirm and expand on these initial findings. Replicationstudies and blinded studies are needed to validate markersidentified. Connecting central and peripheral changes inCNS disorders is key towards defining if and howbiochemical changes in plasma are related to changes inthe brain. Combining metabolomics with imaging ap-proaches and other omics approaches might be powerful

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ways to achieve these goals. Metabolomics has the potentialto enable mapping of early biochemical changes in diseaseand hence provide an opportunity to develop predictivebiomarkers that can trigger earlier interventions. Theinclusion of metabolomics in all steps of drug discoveryand drug development we believe will become a routine asbiochemistry is basic to understanding how drugs work andwhat their safety and efficacy profiles are.

ACKNOWLEDGEMENTS

We thank Katie Jeffress for her assistance with thepreparation of this paper. Supported in part by fundingfrom the National Institutes of Health grants R24GM078233, ‘The Metabolomics Research Network’ (RK-D);National Institute of Neurological Disorders and Stroke andNational Institute of Aging R01NS054008 (RK-D), thenonprofit organizations SMRI (RK-D), NARSAD (RK-D).

DISCLOSURE/CONFLICT OF INTEREST

Dr Kaddurah-Daouk is equity holder in Metabolon Inc., abiotechnology company in the metabolomics domain, andalso holds IP interest in this field. She has received fundingor consultancy fees for BMS, Pfizer, Astra Zeneca, andLundbeck. Dr Krishnan has served as consultant for Amgen,Bristol-Myer Squibb, CeNeRx, Corcept, GlaxoSmithKline,Johnson and Johnson, Lundbeck, Merck, Organon, Pfizer,Sepracor, Wyeth. In addition he is an inventor on patents inthe metabolomics field.

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